Articles · Page 5
Older posts from the archive.
FHIR Meets Graph Databases: Exploring Healthcare's Natural Network Structure
How FHIR's interconnected resources transform into powerful graph relationships. Exploring the potential of graph technologies in healthcare AI at Clarity Health Project.
The Tools I Dropped When AI Changed My Development Workflow
After 12 years of accumulating dev tools, AI coding assistants forced me to rethink every layer of my stack. Here's what I dropped, what I added, and the principle behind the whole thing.
From GPT-2 to DeepSeek: The Architectural Changes That Actually Mattered
I've been reading ML papers for 10 years. Most don't matter. These architectural choices did. RoPE, GQA, SwiGLU — each one solved a real scaling problem. Here's what practitioners need to know when a new model claims 'better architecture.'
Building a GenAI Platform That Doesn't Collapse Under Its Own Weight
Most GenAI platforms fail not because the models are bad, but because teams build everything at once. A practitioner's guide to layered GenAI architecture — from the minimal production-ready core to healthcare-grade guardrails and beyond.
The GenAI Strategy Question You're Not Asking (But Should Be)
Everyone asks 'how should we use GenAI?' The honest answer requires a harder question first: does AI's unique capability actually create new value here, or is it just a more expensive way to do something that already worked? A practitioner's framework for getting this right — especially in healthcare.
Every Failed AI Product Has the Same Root Cause
After 12 years in ML and AI, I keep seeing the same failure pattern: teams that ship fast and iterate on vibes instead of building systematic evaluation systems. Evals are not a nice-to-have — they are the core competency of any serious AI product team.
The 6 Ways I've Watched GenAI Projects Fail (And How to Avoid Them)
After 12 years in ML and two years watching GenAI projects go sideways in healthcare — sometimes with real patient consequences — here are the six failure modes I see over and over again, and what to do instead.
When to Look Beyond Standard LLMs (And When to Stop Overthinking It)
Most teams should use a frontier API and move on. But there are specific situations — extreme latency, long-context scale, cost walls, privacy constraints — where alternative architectures actually matter. Here's the decision framework I use.
When Recommendations Meet Language: The LLM-RecSys Convergence
Most AI stacks treat the recommendation engine and the language model as two separate systems that hand off to each other. A new class of hybrid models eliminates that seam — and the implications for domain-specific AI are significant.